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Application of Bayesian Networks of Genotype by Environment Interaction Evaluation Under Plant Disease, Soil Types and Climate Condition-using Bayesia Lab

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  • Siraj Osman Omer

    (Experimental Design and Analysis Unit, Agricultural Research Corporation (ARC), P.O. Box 126, Wad Medani, Sudan)

Abstract

Genotype by environment interaction (GEI) linked to plant disease, soil properties and climate conditions add potential value for a breeding program to underpin decision making. In understanding genotype x environment interaction, the most challenging factors are the identification of genetic variation for a range of traits and their responsiveness to the climate change factors. In order to study the complex relationships with genetic and non-genetic factors, the application of Bayesian network tools will help understand and accelerate plant breeding progress and improve the efficiency of crop production. In this study, we proposed the application of Bayesian networks (BNs) to evaluate genotype by environment interaction under plant diseases, soil type, and climate variables. An adapted to simulate multiple environmental trial (MET) data of maize (corn) was used to examine the performance of the BN predictive modeling using BayesiaLab for deriving knowledge and graphical structure for exploring GEI diagnosis and analysis. The results highlighted that genotypes have the same probability and the frequentist of rainfall, temperature, soil type, and disease type occurred as

Suggested Citation

  • Siraj Osman Omer, 2021. "Application of Bayesian Networks of Genotype by Environment Interaction Evaluation Under Plant Disease, Soil Types and Climate Condition-using Bayesia Lab," Academic Journal of Applied Mathematical Sciences, Academic Research Publishing Group, vol. 7(3), pages 158-166, 07-2021.
    • Handle: RePEc:arp:ajoams:2021:p:158-166
    DOI: 10.32861/ajams.73.158.166
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    References listed on IDEAS

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    1. SangSik Lee & YiNa Jeong & SuRak Son & ByungKwan Lee, 2019. "A Self-Predictable Crop Yield Platform (SCYP) Based On Crop Diseases Using Deep Learning," Sustainability, MDPI, vol. 11(13), pages 1-21, July.
    2. Musango, J.K. & Peter, C., 2007. "A Bayesian approach towards facilitating climate change adaptation research on the South African agricultural sector," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 46(2), pages 1-15, June.
    3. Mohsen Niazian & Gniewko Niedbała, 2020. "Machine Learning for Plant Breeding and Biotechnology," Agriculture, MDPI, vol. 10(10), pages 1-23, September.
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